XplainLLM: A QA Explanation Dataset for Understanding LLM Decision-Making

• URL: http://arxiv.org/abs/2311.08614v1
• Date: Wed, 15 Nov 2023 00:34:28 GMT
• Title: XplainLLM: A QA Explanation Dataset for Understanding LLM Decision-Making
• Authors: Zichen Chen, Jianda Chen, Mitali Gaidhani, Ambuj Singh, Misha Sra
• Abstract summary: Large Language Models (LLMs) have recently made impressive strides in natural language understanding tasks. In this paper, we look into bringing some transparency to this process by introducing a new explanation dataset. Our dataset includes 12,102 question-answer-explanation (QAE) triples.
• Score: 13.928951741632815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have recently made impressive strides in natural language understanding tasks. Despite their remarkable performance, understanding their decision-making process remains a big challenge. In this paper, we look into bringing some transparency to this process by introducing a new explanation dataset for question answering (QA) tasks that integrates knowledge graphs (KGs) in a novel way. Our dataset includes 12,102 question-answer-explanation (QAE) triples. Each explanation in the dataset links the LLM's reasoning to entities and relations in the KGs. The explanation component includes a why-choose explanation, a why-not-choose explanation, and a set of reason-elements that underlie the LLM's decision. We leverage KGs and graph attention networks (GAT) to find the reason-elements and transform them into why-choose and why-not-choose explanations that are comprehensible to humans. Through quantitative and qualitative evaluations, we demonstrate the potential of our dataset to improve the in-context learning of LLMs, and enhance their interpretability and explainability. Our work contributes to the field of explainable AI by enabling a deeper understanding of the LLMs decision-making process to make them more transparent and thereby, potentially more reliable, to researchers and practitioners alike. Our dataset is available at: https://github.com/chen-zichen/XplainLLM_dataset.git

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